Definition: A computational model inspired by the human brain, consisting of interconnected nodes (neurons) designed to process information.
Better definition: A computer's attempt at building a brain, with lots of interconnected neurons gossiping about data.
Where does this fit in the AI Landscape?
Neural networks form the foundation of many AI and machine learning models. They've enabled remarkable achievements in tasks like image recognition and natural language processing, and they continue to push the boundaries of what machines can learn and accomplish.
What are the real world impacts of this?
Neural Networks are the backbone of many AI systems today, from image recognition in social media to health diagnosis systems. They help in making our digital experiences more efficient and personalized. For AI developers, understanding neural networks is fundamental to building effective and sophisticated AI models.
What could go wrong in the real world with this?
A neural network designed to organize a person's closet ends up creating a complex maze of clothing, only navigable by a select few who understand its intricate layout.
The backbone of many ML models, including GPT. NNs imitate the human brain to identify patterns and relationships in provided data, facilitating both code and language prediction.